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. 2021 Jul 6;26(14):4116.
doi: 10.3390/molecules26144116.

Discovery of New Hits as Antitrypanosomal Agents by In Silico and In Vitro Assays Using Neolignan-Inspired Natural Products from Nectandra leucantha

Affiliations

Discovery of New Hits as Antitrypanosomal Agents by In Silico and In Vitro Assays Using Neolignan-Inspired Natural Products from Nectandra leucantha

Sheila C Araujo et al. Molecules. .

Abstract

In the present study, the phytochemical study of the n-hexane extract from flowers of Nectandra leucantha (Lauraceae) afforded six known neolignans (1-6) as well as one new metabolite (7), which were characterized by analysis of NMR, IR, UV, and ESI-HRMS data. The new compound 7 exhibited potent activity against the clinically relevant intracellular forms of T. cruzi (amastigotes), with an IC50 value of 4.3 μM and no observed mammalian cytotoxicity in fibroblasts (CC50 > 200 μM). Based on the results obtained and our previous antitrypanosomal data of 50 natural and semi-synthetic related neolignans, 2D and 3D molecular modeling techniques were employed to help the design of new neolignan-based compounds with higher activity. The results obtained from the models were important to understand the main structural features related to the biological response of the neolignans and to aid in the design of new neolignan-based compounds with better biological activity. Therefore, the results acquired from phytochemical, biological, and in silico studies showed that the integration of experimental and computational techniques consists of a powerful tool for the discovery of new prototypes for development of new drugs to treat CD.

Keywords: Chagas disease; Trypanosoma cruzi; drug design; in silico; in vitro assays; neolignans.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow used to study new neolignan-based compounds with potential trypanocidal activity from N. leucantha.
Figure 2
Figure 2
Chemical structures of natural (17) and semisynthetic (850) neolignans.
Figure 3
Figure 3
HCA results (dendrogram) for the compound set obtained from (a) drug-like properties and (b) molecular fingerprint. Distribution of compounds in training, test, and total sets according to (c) drug-like properties, (d) pIC50 range, and (e) molecular fingerprint.
Figure 4
Figure 4
Maximum common structure (MCS) considered for the alignment of the molecules: (a) Compound 7 (the most active of the series) was used as a template; (b) general structure of the neolignans with the core used in the molecular alignment.
Figure 5
Figure 5
Alignment of the compound set using the maximum common substructure (MCS) obtained from Distill.
Figure 6
Figure 6
Experimental and predicted pIC50 for the compound sets (training and test) obtained from the HQSAR, CoMFA, and CoMSIA models. Grey squares: Training set; black triangles: Test set.
Figure 7
Figure 7
2D and 3D maps for the more active (7) and one of the less active compounds (26). Green and yellow regions in the HQSAR maps represent substitutions by voluminous groups that can improve the biological activity; orange and red indicate voluminous substituents that can contribute negatively. In addition, green contours in the CoMFA maps suggest that bulky groups can contribute to the biological activity; yellow contours = voluminous groups could decrease the biological activity. In blue regions, positively charged groups can increase the biological property; red regions = negatively charged groups can improve the activity. For the hydrophobic contour maps (CoMSIA): Cyan contours = hydrophobic substituents can enhance the activity; orange contours = hydrophobic groups can decrease the activity. From the CoMSIA maps, green contours suggest that bulky groups can contribute to the biological activity; yellow contours = voluminous groups could decrease the biological activity (favorable contours are set to 80% and the unfavorable regions are set to 20%).
Figure 8
Figure 8
Structure-activity relationships revealed from the HQSAR, CoMFA, and CoMSIA studies applied to compound 7 of the dataset.

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